A Decision Tree Model to Determine Some Environmental Factors Affecting 305-Day Milk Yield in Simmental Cows

Yıl 2022, Cilt: 39 Sayı: 3, 191 - 198, 31.12.2022

Aslı Akıllı Hülya Atıl Çiğdem Takma

https://doi.org/10.55507/gopzfd.1175502

Öz

In this study, some environmental factors thought to be effective on 305-day milk yield in Simmental cows, were examined according to the decision tree method with regression tree algorithm. For this purpose, the effect levels of calving interval, somatic cell count, calving age, and parity variables on the 305-day milk yield of 148 Simmental cows were determined. As a result of the decision tree application, the factors affecting 305-day milk yield were found as parity, calving age, somatic cell count, and calving interval, in order of importance. In addition, it was determined that the 305-day milk yield of the cows with the calving age above 5 was high, and the cows with the somatic cell count greater than 104.500 were found to be the lowest. There is a need to use the decision tree approach in order to examine the effects of other environmental factors that are thought to be effective on milk yield or other economic characteristics in dairy farming and to provide appropriate conditions by correcting the relevant factors accordingly.

Anahtar Kelimeler

Decision Tree, 305-day milk yield, Simmental cow

Simmental İneklerde 305 Günlük Süt Verimini Etkileyen Bazı Çevresel Faktörlerin Belirlenmesine Yönelik Bir Karar Ağacı Modeli

Öz

Bu çalışmada Simmental (Sarı Alaca) ineklerde 305 günlük süt verimi üzerinde etkili olduğu düşünülen bazı çevresel faktörler regresyon ağacı algoritması ile karar ağacı yöntemine göre incelenmiştir. Bu amaçla buzağılama aralığı, somatik hücre sayısı, buzağılama yaşı ve laktasyon sırası değişkenlerinin 148 adet Simmental ırkı ineğin 305 günlük süt verimine etki düzeyleri belirlenmiştir. Karar ağacı uygulaması sonucunda 305 günlük süt verimini etkileyen faktörler önem sırasına göre laktasyon sırası, buzağılama yaşı, somatik hücre sayısı ve buzağılama aralığı olarak bulunmuştur. Ayrıca buzağılama yaşı 5 yıl yaşın üzerinde olan ineklerin 305 günlük süt veriminin yüksek olduğu, somatik hücre sayısı 104,500’ün üzerinde olan ineklerin ise en düşük olduğu belirlenmiştir. Süt hayvancılıkta süt verimi veya diğer ekonomik özellikler üzerine etkili olduğu düşünülen başka çevresel etmenlerin etkilerini incelemek ve buna göre ilgili faktörlerin düzeltilerek uygun şartların sağlanması için karar ağacı yaklaşımının kullanılmasına ihtiyaç vardır.

Anahtar Kelimeler

Karar ağacı, 305 Günlük süt verimi, Simmental inek

Kaynakça

• Aerts, J., Sitkowska, B., Piwczyński, D., Kolenda, M., & Önder, H. (2022a). The optimal level of factors for high daily milk yield in automatic milking system. Livestock Science, 264, 105035. https://doi.org/10.1016/j.livsci.2022.105035.
• Aerts, J., Kolenda, M., Piwczyński, D., Sitkowska, B., & Önder, H. (2022b). Forecasting Milking Efficiency of Dairy Cows Milked in an Automatic Milking System Using the Decision Tree Technique. Animals, 12(8), 1040. https://doi.org/10.3390/ani12081040.
• Akbulut, Ö. (1998). Simental sığırların Türkiye'de verim performansı üzerine bir değerlendirme. Atatürk Üniv. Ziraat Fak. Derg, 29(1), 43-49.
• Aksoy, A. R. (1995). Milk yield of Swiss Brown and Simmental cows in kars goose raising Station. Yuzuncu Yil University, The Journal of Agricultural Faculty (Turkey). 6, 55-57.
• Anonymous, (2020). Food and Agriculture Organization of the United Nations. https://www.fao.org/faostat/en/#data/QCL. Aytekin, İ., & Boztepe, S. (2014). Süt Sığırlarında Somatik Hücre Sayısı, Önemi ve Etki Eden Faktörler, Türk Tarım Gıda Bilim ve Teknoloji Dergisi, (2): 112-121.
• Bakır, G., Keskin, S., & Mirtagioğlu, H. (2010). Determination of the effective factors for 305 days milk yield by Regression Tree (RT) method. Journal of Animal and Veterinary Advances, 9(1), 55-59.
• Barłowska, J., Litwińczuk, Z., Wolanciuk, A., & Brodziak, A. (2009). Relationship of somatic cell count to daily yield and technological usefulness of milk from. Polish Journal of Veterinary Sciences, 12(1), 75-79.
• Bartlett, P. C., Miller, G.Y., Anderson, C.R. & Kirk, J.H. (1990). Milk production and somatic cell count in Michigan dairy herds. J. Dairy Sci., 73, 2794-2800. https://doi.org/10.3168/jds.S0022-0302(90)78966-7.
• Bujko, J., Candrák, J., Strapák, P., Žitný, J., & Hrnčár, C. (2018). Evaluation relation between traits of milk production and calving interval in breeding herds of Slovak Simmental dairy cows. Albanian Journal of Agricultural Sciences, 17(1), 31-36.
• Cziszter, L. T., Gavojdian, D., Neamt, R., Neciu, F., Kusza, S., & Ilie, D. E. (2016). Effects of temperament on production and reproductive performances in Simmental dual-purpose cows. Journal of Veterinary Behavior, 15, 50-55. https://doi.org/10.1016/j.jveb.2016.08.070.
• Çak, B., Keskin, S., & Yılmaz, O. (2013). Regression tree analysis for determining of affecting factors to Lactation Milk Yield in Brown Swiss cattle. Asian Journal of Animal and Veterinary Advances, 8(4), 677-682. Doi: 10.3923/ajava.2013.677.682.
• Çamdeviren, H., Mendeş, M., Özkan, M. M., Toros, F., Şasmaz, T., & Öner, S. (2005). Determination of depression risk factors in children and adolescents by regression tree methodology. Acta Medica Okayama, 59(1), 19-26. 10.18926/AMO/31985.
• Çilek, S., & Tekin, M. E. (2006). Calculation of adjustment factors for standardizing lactations to mature age and 305-day and estimation of heritability and repeatability of standardized milk yield of Simmental cattle reared on Kazova state farm. Turkish Journal of Veterinary & Animal Sciences, 30(3), 283-289.
• Çilek, S., Orhan, H., Kaygısız, A., & Şahin, E. H. (2008). Estimation of breeding values of Anatolian population of Simmental cows using monthly test day milk yields. Archiv. Zootechnica, 11, 79-85.
• Erdem, H., Atasever, S., & Kul, E. (2015). Relations of body condition score with milk yield and reproduction traits in Simmental cows. Large Animal Review, 21: 231-234. 2015.
• Franzoi, M., Manuelian, C. L., Penasa, M., & De Marchi, M. (2020). Effects of somatic cell score on milk yield and mid-infrared predicted composition and technological traits of Brown Swiss, Holstein Friesian, and Simmental cattle breeds. Journal of dairy science, 103(1), 791-804. Doi: 10.3168/jds.2019-16916.
• Genç, S., & Mendes, M. (2021). Determining the Factors Affecting 305-Day Milk Yield of Dairy Cows with Regression Tree. Turkish Journal of Agriculture-Food Science and Technology, 9(6), 1154-1158. Doi: https://doi.org/10.24925/turjaf.v9i6.1154-1158.4384.
• Gültekin, İ. (2019). Afyonkarahisar süt sığırı işletmelerinde bazı döl verimi parametrelerine etkili çevresel faktörler ve ekonomik kayıplar (Master's thesis, Afyon Kocatepe Üniversitesi, Sağlık Bilimleri Enstitüsü).
• IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.
• Irizarry, R. A. (2006). Statistical learning: Algorithmic and nonparametric approaches. Course Notes. Department of Biostatistics Johns Hopkins University, Fourth Term 2005-2006.
• Kalinska, A., Slosarz, J., Golebiewski, M., Wojcik, A. G. A. T. A., Przysucha, T. O. M. A. S. Z., & Kruzinska, B. (2019). Influence of cattle breed and country of origin on milk yield and milk composition in dairy cows. Annals of Warsaw University of Life Sciences-SGGW. Animal Science, 58(1), 21-28. Doi: 10.22630/AAS.2019.58.1.3.
• Kara, N. K., Galiç, A., & Çevik, S. (2021). Simental ırkı ineklerde bazı verim özellikleri ve sağlık sorunları arasındaki ilişkinin belirlenmesi. Akademik Ziraat Dergisi, 10(2), 411-418. https://doi.org/10.29278/azd.881922.
• Kliś, P., Piwczyński, D., Sawa, A., & Sitkowska, B. (2021). Prediction of lactational milk yield of cows based on data recorded by AMS during the periparturient period. Animals, 11(2), 383. https://doi.org/10.3390/ani11020383.
• Koç, A. (2016). A review on Simmental raising: 1. Simmental raising in the World and in Turkey. Journal of Adnan Menderes University Agricultural Faculty, 13(2), 97-102.
• Koçak, S., Tekerli, M., Özbeyaz, C., & Demirhan, I. (2008). Some production traits of Holstein, brown-swiss, and Simmental cattle reared in Lalahan livestock research institute. Journal of Lalahan Livestock Research Institute (Turkey), 48(2), 51-57.
• Lopez-Suarez, M., Armengol, E., Calsamiglia, S., & Castillejos, L. (2018). Using decision trees to extract patterns for dairy culling management. In IFIP international conference on artificial intelligence applications and innovations. Springer New York LLC, pp. 231–239.
• M’hamdi, N., Bouallegue, M., Frouja, S., Ressaissi, Y., Brar, S. K., & Hamouda, M. B. (2012). Effects of environmental factors on milk yield, lactation length and dry period in Tunisian Holstein cows. In Milk Production-An Up-to-Date Overview of Animal Nutrition, Management and Health. IntechOpen.
• Macciotta, N. P. P., Vicario, D., Pulina, G., & Cappio-Borlino, A. (2002). Test day and lactation yield predictions in Italian Simmental cows by ARMA methods. Journal of Dairy Science, 85(11), 3107-3114. https://doi.org/10.3168/jds.S0022-0302(02)74398-1.
• Mostert, B. E., Theron, H. E., & Kanfer, F. H. J. (2001). The effect of calving season and age at calving on production traits of South African dairy cattle. South African Journal of Animal Science, 31(3), 205-214.
• O'Leary, C., & Lynch, C. (2022, June). An Evaluation of Machine Learning Approaches for Milk Volume Prediction in Ireland. In 2022 33rd Irish Signals and Systems Conference (ISSC) (pp. 1-8). IEEE. Doi: 10.1109/ISSC55427.2022.9826160.
• Piwczyński, D., Sitkowska, B., Kolenda, M., Brzozowski, M., Aerts, J., & Schork, P. M. (2020). Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees. Animal Science Journal, 91(1), e13414. https://doi.org/10.1111/asj.13414.
• Sablik, P., Szewczuk, M., Januś, E., & Skrzypiec, A. (2019). Comparison of selected milk production traits of Simmental and Polish Black-and-White cows raised in the buffer zone of Ujście Warty National Park. Landbauforschung, 68(3-4), 45-51. Doi:10.3220/LBF1538729088000.
• Sitkowska, B., Piwczynski, D., Aerts, J., Kolenda, M., & ÖZKAYA, S. (2017). Detection of high levels of somatic cells in milk on farms equippedwith an automatic milking system by decision trees technique. Turkish Journal of Veterinary & Animal Sciences, 41(4), 532-540. Doi: 10.3906/vet-1607-78.
• Slob, N., Catal, C., & Kassahun, A. (2021). Application of machine learning to improve dairy farm management: A systematic literature review. Preventive Veterinary Medicine, 187, 105237. https://doi.org/10.1016/j.prevetmed.2020.105237.
• Steensels, M., Antler, A., Bahr, C., Berckmans, D., Maltz, E., & Halachmi, I. (2016). A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot. Animal, 10(9), 1493-1500. https://doi.org/10.1017/S1751731116000744.
• Şahin, A., & Ulutaş, Z. (2011). Tahirova tarım işletmesi de yetiştirile siyah alaca i ekleri süt ve döl verim özellikleri i etkileye bazı çevresel faktörler. Anadolu Tarım Bilimleri Dergisi, 26(2), 156-168.
• Şekerden, Ö. (1999). Effects of calving season and lactation order on milk yield and milk components in Simmental cows. Turkish Journal of Veterinary & Animal Sciences, 23(7), 79-86.
• Ulutaş, Z., & Sezer, M. (2009). Genetic study of milk production and reproduction traits of local born Simmental cattle in Turkey. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, 26(1), 53-59.
• Ulutaş, Z., Sezer, M., Saatçi, M., & Şahin, A. (2010). Estimation of genetic and phenotypic trends of 305-day milk yield for Simmentals reared in Kazova State Farm in Turkey, Kafkas Univ. Vet. Fak. 16(4), 533-536. Doi:10.9775/kvfd.2009.1083.
• Witten, I. H., & Frank, E. (2005). Data mining; practical machine learning tools and techniques, 2nd edition. Morgan Kaufmann, San Francisco, CA, USA.
• Yordanova, A., Gocheva-Ilieva, S., Kulina, H., Yordanova, L., & Marinov, I. (2015). Classification and regression tree analysis in modeling the milk yield and conformation traits for Holstein cows in Bulgaria. Agricultural Science and Technology, 7(2), 208-213.